"""simple docstring"""


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
	from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
	a				=True
	from torch.cuda.amp import autocast


a				=logging.getLogger(__name__)






@dataclass
class   __UpperCAmelCase		:
				A__ :  str    				=			field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
				A__ :  Optional[str]    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
				A__ :  Optional[bool]    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}      )
				A__ :  Optional[bool]    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''Whether to log verbose messages or not.'''} , )
				A__ :  Optional[float]    				=			field(
				    default=2.0 , metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}      )
				A__ :  Optional[float]    				=			field(
				    default=0.5 , metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}      )
				A__ :  Optional[float]    				=			field(
				    default=0.99_99_95 , metadata={'''help''': '''Decay of gumbel temperature during training.'''}      )





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,       datefmt="%m/%d/%Y %H:%M:%S"  ,       handlers=[logging.StreamHandler(sys.stdout							)]  ,       )
						lowerCamelCase__   =logging.WARNING
						if model_args.verbose_logging:
												lowerCamelCase__   =logging.DEBUG
						elif trainer_utils.is_main_process(training_args.local_rank							):
												lowerCamelCase__   =logging.INFO
						logger.setLevel(__lowerCAmelCase							)






@dataclass
class   __UpperCAmelCase		:
				A__ :  str    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}      )
				A__ :  Optional[str]    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
				A__ :  Optional[str]    				=			field(
				    default='''train''' , metadata={
				        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
				    } , )
				A__ :  Optional[str]    				=			field(
				    default='''validation''' , metadata={
				        '''help''': (
				            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
				        )
				    } , )
				A__ :  Optional[str]    				=			field(
				    default='''file''' , metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} , )
				A__ :  bool    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
				A__ :  Optional[int]    				=			field(
				    default=1 , metadata={
				        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
				    } , )
				A__ :  Optional[int]    				=			field(
				    default=__lowerCAmelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
				A__ :  Optional[float]    				=			field(
				    default=20.0 , metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}      )






@dataclass
class   __UpperCAmelCase		:
				A__ :  WavaVecaForPreTraining
				A__ :  WavaVecaFeatureExtractor
				A__ :  Union[bool, str]    				=			"longest"
				A__ :  Optional[int]    				=			None
				A__ :  Optional[int]    				=			None

				def __call__( self      ,					_lowerCamelCase ):
										# reformat list to dict and set to pytorch format
										lowerCamelCase__   =self.feature_extractor.pad(
										    _lowerCamelCase      ,					max_length=self.max_length      ,					padding=self.padding      ,					pad_to_multiple_of=self.pad_to_multiple_of      ,					return_tensors="pt"      ,					)
										lowerCamelCase__   =self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )

										lowerCamelCase__   =batch["input_values"].shape[0]

										# make sure that no loss is computed on padded inputs
										if batch["attention_mask"] is not None:
																# compute real output lengths according to convolution formula
																lowerCamelCase__   =self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
																    torch.long )

																lowerCamelCase__   =torch.zeros(
																    (batch_size, mask_indices_seq_length)      ,					dtype=torch.long      ,					device=batch["input_values"].device )

																# these two operations makes sure that all values
																# before the output lengths indices are attended to
																lowerCamelCase__   =1
																lowerCamelCase__   =attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()

										# sample randomly masked indices
										lowerCamelCase__   =_compute_mask_indices(
										    (batch_size, mask_indices_seq_length)      ,					self.model.config.mask_time_prob      ,					self.model.config.mask_time_length      ,					attention_mask=_lowerCamelCase      ,					min_masks=2      ,					)

										return batch






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					*_lowerCamelCase      ,					_lowerCamelCase=1      ,					_lowerCamelCase=0      ,					_lowerCamelCase=1.0      ,					**_lowerCamelCase ):
										super().__init__(*_lowerCamelCase      ,					**_lowerCamelCase )
										lowerCamelCase__   =0
										lowerCamelCase__   =max_gumbel_temp
										lowerCamelCase__   =min_gumbel_temp
										lowerCamelCase__   =gumbel_temp_decay






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										model.train()
										lowerCamelCase__   =self._prepare_inputs(_lowerCamelCase )

										if self.use_amp:
																with autocast():
																						lowerCamelCase__   =self.compute_loss(_lowerCamelCase      ,					_lowerCamelCase )
										else:
																lowerCamelCase__   =self.compute_loss(_lowerCamelCase      ,					_lowerCamelCase )

										if self.args.n_gpu > 1 or self.deepspeed:
																if model.module.config.ctc_loss_reduction == "mean":
																						lowerCamelCase__   =loss.mean()
																elif model.module.config.ctc_loss_reduction == "sum":
																						lowerCamelCase__   =loss.sum() / (inputs["mask_time_indices"]).sum()
																else:
																						raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''' )

										if self.args.gradient_accumulation_steps > 1:
																lowerCamelCase__   =loss / self.args.gradient_accumulation_steps

										if self.use_amp:
																self.scaler.scale(_lowerCamelCase ).backward()
										elif self.use_apex:
																with amp.scale_loss(_lowerCamelCase      ,					self.optimizer ) as scaled_loss:
																						scaled_loss.backward()
										elif self.deepspeed:
																self.deepspeed.backward(_lowerCamelCase )
										else:
																loss.backward()

										self.num_update_step += 1
										# make sure gumbel softmax temperature is decayed
										if self.args.n_gpu > 1 or self.deepspeed:
																model.module.set_gumbel_temperature(
																    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,					self.min_gumbel_temp ) )
										else:
																model.set_gumbel_temperature(
																    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,					self.min_gumbel_temp ) )

										return loss.detach()





def      lowerCamelCase_					(   )				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)

						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =parser.parse_args_into_dataclasses()
						configure_logger(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Downloading and loading a dataset from the hub.
						lowerCamelCase__   =load_dataset(data_args.dataset_name  ,       data_args.dataset_config_name  ,       cache_dir=model_args.cache_dir							)

						if "validation" not in datasets.keys():
												# make sure only "validation" and "train" keys remain"
												lowerCamelCase__   =DatasetDict()
												lowerCamelCase__   =load_dataset(
												    data_args.dataset_name  ,       data_args.dataset_config_name  ,       split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''  ,       cache_dir=model_args.cache_dir  ,       )
												lowerCamelCase__   =load_dataset(
												    data_args.dataset_name  ,       data_args.dataset_config_name  ,       split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''  ,       cache_dir=model_args.cache_dir  ,       )
						else:
												# make sure only "validation" and "train" keys remain"
												lowerCamelCase__   =DatasetDict()
												lowerCamelCase__   =load_dataset(
												    data_args.dataset_name  ,       data_args.dataset_config_name  ,       split="validation"  ,       cache_dir=model_args.cache_dir  ,       )
												lowerCamelCase__   =load_dataset(
												    data_args.dataset_name  ,       data_args.dataset_config_name  ,       split=F'''{data_args.train_split_name}'''  ,       cache_dir=model_args.cache_dir  ,       )

						# only normalized-inputs-training is supported
						lowerCamelCase__   =WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path  ,       cache_dir=model_args.cache_dir  ,       do_normalize=__lowerCAmelCase							)

						def prepare_dataset(__lowerCAmelCase							):
												# check that all files have the correct sampling rate
												lowerCamelCase__   , lowerCamelCase__   =librosa.load(batch[data_args.speech_file_column]  ,       sr=feature_extractor.sampling_rate							)
												return batch

						# load audio files into numpy arrays
						lowerCamelCase__   =datasets.map(
						    __lowerCAmelCase  ,       num_proc=data_args.preprocessing_num_workers  ,       remove_columns=datasets["train"].column_names							)

						# filter audio files that are too long
						lowerCamelCase__   =vectorized_datasets.filter(
						    lambda __lowerCAmelCase							: len(data["speech"]							) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate							)							)

						def normalize(__lowerCAmelCase							):
												return feature_extractor(batch["speech"]  ,       sampling_rate=feature_extractor.sampling_rate							)

						# normalize and transform to `BatchFeatures`
						lowerCamelCase__   =vectorized_datasets.map(
						    __lowerCAmelCase  ,       batched=__lowerCAmelCase  ,       num_proc=data_args.preprocessing_num_workers  ,       load_from_cache_file=not data_args.overwrite_cache  ,       remove_columns=vectorized_datasets["train"].column_names  ,       )

						# pretraining is only supported for "newer" stable layer norm architecture
						# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
						lowerCamelCase__   =WavaVecaConfig.from_pretrained(
						    model_args.model_name_or_path  ,       cache_dir=model_args.cache_dir  ,       gradient_checkpointing=training_args.gradient_checkpointing  ,       )

						if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
												raise ValueError(
												    "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
												    " ``config.feat_extract_norm='layer'"							)

						lowerCamelCase__   =WavaVecaForPreTraining(__lowerCAmelCase							)

						lowerCamelCase__   =DataCollatorForWavaVecaPretraining(model=__lowerCAmelCase  ,       feature_extractor=__lowerCAmelCase							)

						lowerCamelCase__   =WavaVecaPreTrainer(
						    model=__lowerCAmelCase  ,       data_collator=__lowerCAmelCase  ,       args=__lowerCAmelCase  ,       train_dataset=vectorized_datasets["train"]  ,       eval_dataset=vectorized_datasets["validation"]  ,       tokenizer=__lowerCAmelCase  ,       max_gumbel_temp=model_args.max_gumbel_temperature  ,       min_gumbel_temp=model_args.min_gumbel_temperature  ,       gumbel_temp_decay=model_args.gumbel_temperature_decay  ,       )
						trainer.train()


if __name__ == "__main__":
	main()



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase  ,       __lowerCAmelCase							):
												raise TypeError("only integers accepted as input"							)
						else:
												lowerCamelCase__   =str(abs(__lowerCAmelCase							)							)
												lowerCamelCase__   =[list(__lowerCAmelCase							) for char in range(len(__lowerCAmelCase							)							)]
												for index in range(len(__lowerCAmelCase							)							):
																		num_transpositions[index].pop(__lowerCAmelCase							)
												return max(
												    int("".join(list(__lowerCAmelCase							)							)							) for transposition in num_transpositions							)


if __name__ == "__main__":
	__import__('doctest').testmod()


